Abstract This program will design, implement, and establish a course to teach advanced statistical methods in neuroimaging and genetics. Over the past decade the National Institutes of Health has greatly increased funding of grants that utilized advanced neuroimaging methods, genetic methods, and advanced statistical methods. While introductory courses are offered, there is, however, no course offered in the United States that provides an intensive, hands-on learning opportunity to better prepare biomedical and clinical researchers in advanced methods. Given that the combined budgets for grants that utilize these advanced analysis techniques from the National Institute of Neurological Disorders and Stroke (NINDS), National Institute of Mental Health (NIMH), National Institute on Aging (NIA), National Institute on Drug Abuse (NIDA), and National Institute of Biomedical Imaging and Bioengineering (NIBIB (five Institutes that greatly utilize neuroimaging and genetics), have grown 5-fold from ~250 million USD to ~1250 million USD, there is a great need to provide an educational opportunity to ensure the workforce is well positioned to carry out important work that has been identified by these and other institutes. This program will create the course, ?Training in Advanced Statistical Methods in Neuroimaging and Genetics?. It will bring together world-class scientists and educators in a two-week intensive format to provide theoretical lectures paired with hands-on computer tutorials. This program will teach 20 students in the first year, 25 students in years 2 and 3, and then 30 students in years 4 and 5. These students will be accepted from all across the United States, with attention to a diverse student cohort, ensuring enrollment of women and underrepresented minorities. Additionally, the project will distribute Travel Awards based on financial need. The beginning of the course will be a full-day primer on basic statistical methods and analytic infrastructure. Over two weeks, students will learn and put into practice methods such as: hierarchical statistical models, Bayesian statistics, network science, functional and structural connectomics, disease driven degeneration of the brain, and methods for analysis of genetics data such as polygenic risk scoring and structural equation modeling. The course will conclude with lectures and labs on multi-modal analysis, including multi-modal imaging analysis, and imaging-genetics analysis, along with classification methods for biomarker development. To help students apply the acquired knowledge and skills to their independent research projects back at their home institutes, we will supplement the course with a continuing education portion. For the 6 months following the formal two-week course, the students will have access to the theory lecturers on a monthly basis in a structured learning environment. Additionally, during that 6-month follow-up period, they will also have near-real-time access regarding technical implementation questions through the Slack messaging and collaboration platform. This continued education portion will greatly increase success utilizing their new practical skills in their own research.